Overview

Dataset statistics

Number of variables21
Number of observations452
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.3 KiB
Average record size in memory168.3 B

Variable types

CAT14
NUM7

Warnings

id.orig_h has constant value "452" Constant
local_orig has constant value "452" Constant
local_resp has constant value "452" Constant
label has constant value "452" Constant
duration has a high cardinality: 328 distinct values High cardinality
orig_ip_bytes is highly correlated with orig_pktsHigh correlation
orig_pkts is highly correlated with orig_ip_bytesHigh correlation
resp_ip_bytes is highly correlated with resp_pktsHigh correlation
resp_pkts is highly correlated with resp_ip_bytesHigh correlation
proto is highly correlated with id.resp_h and 3 other fieldsHigh correlation
id.resp_h is highly correlated with protoHigh correlation
orig_bytes is highly correlated with proto and 2 other fieldsHigh correlation
service is highly correlated with orig_bytes and 1 other fieldsHigh correlation
resp_bytes is highly correlated with proto and 1 other fieldsHigh correlation
conn_state is highly correlated with orig_bytes and 1 other fieldsHigh correlation
history is highly correlated with proto and 1 other fieldsHigh correlation
orig_ip_bytes is highly skewed (γ1 = 21.10297932) Skewed
resp_pkts is highly skewed (γ1 = 21.25341521) Skewed
resp_ip_bytes is highly skewed (γ1 = 21.13153196) Skewed
ts has unique values Unique
uid has unique values Unique
missed_bytes has 445 (98.5%) zeros Zeros
resp_pkts has 14 (3.1%) zeros Zeros
resp_ip_bytes has 14 (3.1%) zeros Zeros

Reproduction

Analysis started2022-04-08 02:57:35.052096
Analysis finished2022-04-08 02:57:43.222995
Duration8.17 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

ts
Categorical

UNIQUE

Distinct452
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
1540469302.538640
 
1
1540519325.009016
 
1
1540523840.194421
 
1
1540523840.251867
 
1
1540522940.130301
 
1
Other values (447)
447 
ValueCountFrequency (%) 
1540469302.53864010.2%
 
1540519325.00901610.2%
 
1540523840.19442110.2%
 
1540523840.25186710.2%
 
1540522940.13030110.2%
 
1540522940.18724810.2%
 
1540522924.94893210.2%
 
1540522924.94793410.2%
 
1540522924.84823910.2%
 
1540522924.79104410.2%
 
Other values (442)44297.8%
 
2022-04-07T21:57:43.339829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique452 ?
Unique (%)100.0%
2022-04-07T21:57:43.589582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length17
Mean length17
Min length17

uid
Categorical

UNIQUE

Distinct452
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
CGm6jB4dXK71ZDWUDh
 
1
C8k0ZJ3nN2F72zMFkk
 
1
CdYm3Z1mg8aMUUU5z8
 
1
CbeaqN34EdECULHYvg
 
1
CfsVBw4Ck8w5UNxUBk
 
1
Other values (447)
447 
ValueCountFrequency (%) 
CGm6jB4dXK71ZDWUDh10.2%
 
C8k0ZJ3nN2F72zMFkk10.2%
 
CdYm3Z1mg8aMUUU5z810.2%
 
CbeaqN34EdECULHYvg10.2%
 
CfsVBw4Ck8w5UNxUBk10.2%
 
CSrevYRCgCuOIGzh810.2%
 
CCW3831vLWWHnpR76810.2%
 
CEJoxH3YHDX6tGKDRd10.2%
 
Cu5sdC3zirq7VRrm7f10.2%
 
CmH8Jx22E27bJRQZX810.2%
 
Other values (442)44297.8%
 
2022-04-07T21:57:43.716526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique452 ?
Unique (%)100.0%
2022-04-07T21:57:43.816748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length18
Mean length17.73672566
Min length15

id.orig_h
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
192.168.1.132
452 
ValueCountFrequency (%) 
192.168.1.132452100.0%
 
2022-04-07T21:57:43.914225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-07T21:57:43.961119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:44.008357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length13
Min length13

id.orig_p
Real number (ℝ≥0)

Distinct438
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48306.52212
Minimum68
Maximum60977
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2022-04-07T21:57:44.103308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile33787.85
Q141905
median52594
Q356663.25
95-th percentile60642.25
Maximum60977
Range60909
Interquartile range (IQR)14758.25

Descriptive statistics

Standard deviation11877.4796
Coefficient of variation (CV)0.2458773491
Kurtosis5.425613441
Mean48306.52212
Median Absolute Deviation (MAD)5748
Skewness-2.006986542
Sum21834548
Variance141074521.7
MonotocityNot monotonic
2022-04-07T21:57:44.213887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1900132.9%
 
3512820.4%
 
6820.4%
 
4076610.2%
 
5367510.2%
 
3469810.2%
 
5367410.2%
 
5593010.2%
 
5858410.2%
 
3608810.2%
 
Other values (428)42894.7%
 
ValueCountFrequency (%) 
6820.4%
 
1900132.9%
 
3281210.2%
 
3289310.2%
 
3318510.2%
 
ValueCountFrequency (%) 
6097710.2%
 
6096610.2%
 
6090210.2%
 
6090110.2%
 
6090010.2%
 

id.resp_h
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
192.168.1.1
193 
2.16.60.82
47 
2.16.60.139
39 
216.239.35.12
27 
216.239.35.4
26 
Other values (10)
120 
ValueCountFrequency (%) 
192.168.1.119342.7%
 
2.16.60.824710.4%
 
2.16.60.139398.6%
 
216.239.35.12276.0%
 
216.239.35.4265.8%
 
216.239.35.8265.8%
 
216.239.35.0255.5%
 
52.215.95.120224.9%
 
52.209.221.67214.6%
 
239.255.255.250132.9%
 
Other values (5)132.9%
 
2022-04-07T21:57:44.344509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.7%
2022-04-07T21:57:44.438630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length11
Mean length11.54424779
Min length10

id.resp_p
Real number (ℝ≥0)

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.4446903
Minimum53
Maximum1900
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2022-04-07T21:57:44.517249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile53
Q153
median80
Q3123
95-th percentile443
Maximum1900
Range1847
Interquartile range (IQR)70

Descriptive statistics

Standard deviation327.3297246
Coefficient of variation (CV)1.624911156
Kurtosis18.23972042
Mean201.4446903
Median Absolute Deviation (MAD)27
Skewness4.074979924
Sum91053
Variance107144.7486
MonotocityNot monotonic
2022-04-07T21:57:44.596604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
5319142.3%
 
12310423.0%
 
4438819.5%
 
805411.9%
 
1900132.9%
 
6720.4%
 
ValueCountFrequency (%) 
5319142.3%
 
6720.4%
 
805411.9%
 
12310423.0%
 
4438819.5%
 
ValueCountFrequency (%) 
1900132.9%
 
4438819.5%
 
12310423.0%
 
805411.9%
 
6720.4%
 

proto
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
udp
310 
tcp
142 
ValueCountFrequency (%) 
udp31068.6%
 
tcp14231.4%
 
2022-04-07T21:57:44.689279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-07T21:57:44.736541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:44.802548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

service
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
-
205 
dns
191 
http
54 
dhcp
 
2
ValueCountFrequency (%) 
-20545.4%
 
dns19142.3%
 
http5411.9%
 
dhcp20.4%
 
2022-04-07T21:57:44.902445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-07T21:57:44.979455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:45.057992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length2.216814159
Min length1

duration
Categorical

HIGH CARDINALITY

Distinct328
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
0.000499
 
17
0.000500
 
16
0.003497
 
14
0.011492
 
11
0.011493
 
8
Other values (323)
386 
ValueCountFrequency (%) 
0.000499173.8%
 
0.000500163.5%
 
0.003497143.1%
 
0.011492112.4%
 
0.01149381.8%
 
0.00349661.3%
 
0.00050161.3%
 
0.00049851.1%
 
0.00075040.9%
 
0.00324840.9%
 
Other values (318)36179.9%
 
2022-04-07T21:57:45.184894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique282 ?
Unique (%)62.4%
2022-04-07T21:57:45.285385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length8
Mean length8.061946903
Min length1

orig_bytes
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
48
103 
0
87 
34
87 
37
86 
1847
40 
Other values (27)
49 
ValueCountFrequency (%) 
4810322.8%
 
08719.2%
 
348719.2%
 
378619.0%
 
1847408.8%
 
41122.7%
 
4661.3%
 
159530.7%
 
376830.7%
 
184620.4%
 
Other values (22)235.1%
 
2022-04-07T21:57:45.395164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique21 ?
Unique (%)4.6%
2022-04-07T21:57:45.489824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length2
Mean length2.130530973
Min length1

resp_bytes
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
48
103 
0
100 
297
59 
791
22 
100
22 
Other values (23)
146 
ValueCountFrequency (%) 
4810322.8%
 
010022.1%
 
2975913.1%
 
791224.9%
 
100224.9%
 
132224.9%
 
1539214.6%
 
179214.6%
 
442214.6%
 
311204.4%
 
Other values (18)419.1%
 
2022-04-07T21:57:45.583953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)1.8%
2022-04-07T21:57:45.678085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length2.365044248
Min length1

conn_state
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
SF
436 
S0
 
14
RSTR
 
1
OTH
 
1
ValueCountFrequency (%) 
SF43696.5%
 
S0143.1%
 
RSTR10.2%
 
OTH10.2%
 
2022-04-07T21:57:45.772606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.4%
2022-04-07T21:57:45.834984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:45.913608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.006637168
Min length2

local_orig
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
-
452 
ValueCountFrequency (%) 
-452100.0%
 
2022-04-07T21:57:46.007749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-07T21:57:46.055390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:46.107562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

local_resp
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
-
452 
ValueCountFrequency (%) 
-452100.0%
 
2022-04-07T21:57:46.196452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-07T21:57:46.259371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:46.306739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

missed_bytes
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.23230088
Minimum0
Maximum7363
Zeros445
Zeros (%)98.5%
Memory size3.5 KiB
2022-04-07T21:57:46.369273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7363
Range7363
Interquartile range (IQR)0

Descriptive statistics

Standard deviation440.0602524
Coefficient of variation (CV)9.728893817
Kurtosis188.8288646
Mean45.23230088
Median Absolute Deviation (MAD)0
Skewness12.8332795
Sum20445
Variance193653.0257
MonotocityNot monotonic
2022-04-07T21:57:46.583317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
044598.5%
 
153940.9%
 
400610.2%
 
292010.2%
 
736310.2%
 
ValueCountFrequency (%) 
044598.5%
 
153940.9%
 
292010.2%
 
400610.2%
 
736310.2%
 
ValueCountFrequency (%) 
736310.2%
 
400610.2%
 
292010.2%
 
153940.9%
 
044598.5%
 

history
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Dd
296 
ShAFf
76 
ShADadfF
 
23
ShADadtfF
 
18
D
 
14
Other values (9)
 
25
ValueCountFrequency (%) 
Dd29665.5%
 
ShAFf7616.8%
 
ShADadfF235.1%
 
ShADadtfF184.0%
 
D143.1%
 
ShAFaf112.4%
 
ShADacfgF40.9%
 
ShADadFf30.7%
 
ShADafdtF20.4%
 
ShADacgdFf10.2%
 
Other values (4)40.9%
 
2022-04-07T21:57:46.678171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)1.1%
2022-04-07T21:57:46.772369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length2
Mean length3.35840708
Min length1

orig_pkts
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.61061947
Minimum1
Maximum8124
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2022-04-07T21:57:46.850888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile7
Maximum8124
Range8123
Interquartile range (IQR)3

Descriptive statistics

Standard deviation412.0304476
Coefficient of variation (CV)13.91495534
Kurtosis340.4733873
Mean29.61061947
Median Absolute Deviation (MAD)0
Skewness17.99008783
Sum13384
Variance169769.0897
MonotocityNot monotonic
2022-04-07T21:57:46.945014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
129765.7%
 
45211.5%
 
5357.7%
 
7276.0%
 
6235.1%
 
1240.9%
 
820.4%
 
19210.2%
 
9010.2%
 
4810.2%
 
Other values (9)92.0%
 
ValueCountFrequency (%) 
129765.7%
 
45211.5%
 
5357.7%
 
6235.1%
 
7276.0%
 
ValueCountFrequency (%) 
812410.2%
 
329110.2%
 
19210.2%
 
18610.2%
 
16810.2%
 

orig_ip_bytes
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct37
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7543.911504
Minimum62
Maximum2778408
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2022-04-07T21:57:47.039143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile62
Q165
median76
Q3172
95-th percentile2151
Maximum2778408
Range2778346
Interquartile range (IQR)107

Descriptive statistics

Standard deviation130951.1101
Coefficient of variation (CV)17.35851621
Kurtosis447.4120307
Mean7543.911504
Median Absolute Deviation (MAD)14
Skewness21.10297932
Sum3409848
Variance1.714819325e+10
MonotocityNot monotonic
2022-04-07T21:57:47.133271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
7610423.0%
 
628719.2%
 
658619.0%
 
1725211.5%
 
212347.5%
 
2099184.0%
 
69122.7%
 
2139122.7%
 
2151102.2%
 
7461.3%
 
Other values (27)316.9%
 
ValueCountFrequency (%) 
628719.2%
 
658619.0%
 
69122.7%
 
7461.3%
 
7610423.0%
 
ValueCountFrequency (%) 
277840810.2%
 
15726310.2%
 
6566410.2%
 
6361210.2%
 
5745610.2%
 

resp_pkts
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct12
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.805309735
Minimum0
Maximum2667
Zeros14
Zeros (%)3.1%
Memory size3.5 KiB
2022-04-07T21:57:47.212343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum2667
Range2667
Interquartile range (IQR)1

Descriptive statistics

Standard deviation125.3688029
Coefficient of variation (CV)16.06198948
Kurtosis451.8044618
Mean7.805309735
Median Absolute Deviation (MAD)0
Skewness21.25341521
Sum3528
Variance15717.33673
MonotocityNot monotonic
2022-04-07T21:57:47.306533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
129665.5%
 
34510.0%
 
2429.3%
 
6235.1%
 
7204.4%
 
0143.1%
 
561.3%
 
420.4%
 
1210.2%
 
810.2%
 
Other values (2)20.4%
 
ValueCountFrequency (%) 
0143.1%
 
129665.5%
 
2429.3%
 
34510.0%
 
420.4%
 
ValueCountFrequency (%) 
266710.2%
 
1210.2%
 
1010.2%
 
810.2%
 
7204.4%
 

resp_ip_bytes
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct37
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581.8517699
Minimum0
Maximum129675
Zeros14
Zeros (%)3.1%
Memory size3.5 KiB
2022-04-07T21:57:47.405788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile76
Q176
median144
Q3325
95-th percentile1043
Maximum129675
Range129675
Interquartile range (IQR)249

Descriptive statistics

Standard deviation6097.843503
Coefficient of variation (CV)10.48006351
Kurtosis448.3253249
Mean581.8517699
Median Absolute Deviation (MAD)68
Skewness21.13153196
Sum262997
Variance37183695.39
MonotocityNot monotonic
2022-04-07T21:57:47.526223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
7610322.8%
 
3255913.1%
 
92429.3%
 
144357.7%
 
128224.9%
 
160224.9%
 
207214.6%
 
470214.6%
 
339204.4%
 
0143.1%
 
Other values (27)9320.6%
 
ValueCountFrequency (%) 
0143.1%
 
6930.7%
 
7610322.8%
 
8540.9%
 
92429.3%
 
ValueCountFrequency (%) 
12967510.2%
 
183181.8%
 
182392.0%
 
175510.2%
 
142910.2%
 

label
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
- benign -
452 
ValueCountFrequency (%) 
- benign -452100.0%
 
2022-04-07T21:57:47.622958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-07T21:57:47.685459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:47.732723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length14
Mean length14
Min length14

Interactions

2022-04-07T21:57:37.270868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:37.401099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:37.592128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:37.691380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:37.780034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:37.891832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:37.991667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.095619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.205442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.317180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.426752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.533231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.627445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.752884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.851521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:38.949118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.043313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.173656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.283471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.377629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.471803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.565941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.684404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.806601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:39.904284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.043993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.226564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.321395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.416140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.496768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.609695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.698258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.792006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.870512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:40.964700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.078398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.173260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.266870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.380231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.498006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.585463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.699823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.777964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.872126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:41.971142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:42.087014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:42.206358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:42.339423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:42.450657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-07T21:57:47.806883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-07T21:57:47.921150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-07T21:57:48.030945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-07T21:57:48.171915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-04-07T21:57:48.307823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-04-07T21:57:42.689514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-07T21:57:43.072996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

tsuidid.orig_hid.orig_pid.resp_hid.resp_pprotoservicedurationorig_bytesresp_bytesconn_statelocal_origlocal_respmissed_byteshistoryorig_pktsorig_ip_bytesresp_pktsresp_ip_byteslabel
01540469302.538640CGm6jB4dXK71ZDWUDh192.168.1.13258687.0216.239.35.4123.0udp-0.1141844848SF--0.0Dd1.076.01.076.0- benign -
11540469197.400159CnaDAG3n5r8eiG4su2192.168.1.1321900.0239.255.255.2501900.0udp-160.36757975360S0--0.0D24.08208.00.00.0- benign -
21540469385.734089CUrxU238nt0m6yTgKf192.168.1.13232893.0216.239.35.8123.0udp-0.0169864848SF--0.0Dd1.076.01.076.0- benign -
31540469831.302625CGQf8t1kjdxB5PHXL4192.168.1.13253395.02.16.60.82443.0tcp-0.00349700SF--0.0ShAFf5.0212.03.0144.0- benign -
41540469831.265405CUo9DH2QDnCaBIGjkg192.168.1.13252801.0192.168.1.153.0udpdns0.03672434311SF--0.0Dd1.062.01.0339.0- benign -
51540469418.379528CAvXOZ3htimWEtglii192.168.1.1321900.0239.255.255.2501900.0udp-384.518261150720S0--0.0D48.016416.00.00.0- benign -
61540470081.850824CfJsUD2NGQvnK2p7Vd192.168.1.13258124.0216.239.35.12123.0udp-0.2703324848SF--0.0Dd1.076.01.076.0- benign -
71540470187.222098CVwKZS98dRvk1jeH2192.168.1.13235313.0216.239.35.0123.0udp-0.1114294848SF--0.0Dd1.076.01.076.0- benign -
81540470355.430009CzbHG4aoHRooWvyMg192.168.1.13246064.0216.239.35.4123.0udp-0.1144334848SF--0.0Dd1.076.01.076.0- benign -
91540470419.608808CbTB0B2ZnnDWLAIml3192.168.1.13245230.0216.239.35.8123.0udp-0.0169844848SF--0.0Dd1.076.01.076.0- benign -

Last rows

tsuidid.orig_hid.orig_pid.resp_hid.resp_pprotoservicedurationorig_bytesresp_bytesconn_statelocal_origlocal_respmissed_byteshistoryorig_pktsorig_ip_bytesresp_pktsresp_ip_byteslabel
4421540546342.573531CXE9W73ARSXvekc7zl192.168.1.13253721.02.16.60.82443.0tcp-0.01149200SF--0.0ShAFf4.0172.02.092.0- benign -
4431540546342.514567CLmNqS1wVzSMQE0Ci6192.168.1.13238092.0192.168.1.153.0udpdns0.05848534311SF--0.0Dd1.062.01.0339.0- benign -
4441540546435.585552CRBVbs1mwnvbI8ZHjg192.168.1.13258805.0216.239.35.8123.0udp-0.0169694848SF--0.0Dd1.076.01.076.0- benign -
4451540546651.702119C7eJcB3DaLYw7Knw09192.168.1.13255520.0216.239.35.0123.0udp-0.1094274848SF--0.0Dd1.076.01.076.0- benign -
4461540546960.912617CDjRbg4l3sbCPrEpmh192.168.1.13235983.0216.239.35.4123.0udp-0.1166804848SF--0.0Dd1.076.01.076.0- benign -
4471540547242.647507CaITg437HfJAXTXZ44192.168.1.13253722.02.16.60.82443.0tcp-0.01149300SF--0.0ShAFf4.0172.02.092.0- benign -
4481540547242.580300ChD10Y1fVGrFvMpD6g192.168.1.13243799.0192.168.1.153.0udpdns0.06670934297SF--0.0Dd1.062.01.0325.0- benign -
4491540547904.130404Caxyou4MZpRXfDBG8h192.168.1.13251285.0216.239.35.12123.0udp-0.2701074848SF--0.0Dd1.076.01.076.0- benign -
4501540469198.477548CkHQfG3B034MyvP3W192.168.1.13237653.0104.155.18.91443.0tcp-78840.3293052562422995OTH--0.0AaDd3291.0157263.02667.0129675.0- benign -
4511540477674.054085ClZeN63S7LbgEs2ZNk192.168.1.1321900.0239.255.255.2501900.0udp-70334.10574225509360S0--0.0D8124.02778408.00.00.0- benign -